A SVR-Based multiple modeling algorithm for antibiotic fermentation process using FCM

Authors:
Yaofeng Xue;Jingqi Yuan
Affiliations:
Department of Automation, Shanghai Jiao Tong University, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 4
Cited 0

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory

The nature of statistical learning theory
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

A multiple modeling algorithm for antibiotic fermentation process based on fuzzy c-means (FCM) and support vector regression (SVR) is proposed. By analyzing the features of antibiotic fermentation, the mechanism of multiple modeling of the bioprocess is presented. Using FCM clustering method, the bioprocess is classified into several work states and sub-models. Then, taking advantage of the generalization properties of SVR, the multiple model of bioprocess is established and the proposed algorithm is described. Experimental data of industrial penicillin production is used to validate the model.